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Detection of Real-Time Distributed Denial-of-Service (DDoS) Attacks on Internet of Things (IoT) Networks Using Machine Learning Algorithms

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Computers, Materials & Continua 2024年第8期80卷 2139-2159页

作者： Zaed Mahdi Nada Abdalhussien naba mahmood Rana Zaki Information Technology Center University of TechnologyBaghdad00964Iraq Continuous Education Center University of TechnologyBaghdad00964Iraq Computer Science Department University of TechnologyBaghdad00964Iraq

The primary concern of modern technology is cyber attacks targeting the Internet of *** it is one of the most widely used networks today and vulnerable to ***-time threats pose with modern cyber attacks that pose a great danger to the Internet of Things(IoT)networks,as devices can be monitored or service isolated from them and affect users in one way or *** Internet of Things networks is an important matter,as it requires the use of modern technologies and methods,and real and up-to-date data to design and train systems to keep pace with the modernity that attackers use to confront these *** of the most common types of attacks against IoT devices is Distributed Denial-of-Service(DDoS)*** paper makes a unique contribution that differs from existing studies,in that we use recent data that contains real traffic and real attacks on IoT *** a hybrid method for selecting relevant features,And also how to choose highly efficient *** gives the model a high ability to detect distributed denial-of-service *** model proposed is based on a two-stage process:selecting essential features and constructing a detection model using the K-neighbors algorithm with two classifier algorithms logistic regression and Stochastic Gradient Descent classifier(SGD),combining these classifiers through ensemble machine learning(stacking),and optimizing parameters through Grid Search-CV to enhance system *** were conducted to evaluate the effectiveness of the proposed model using the CIC-IoT2023 and CIC-DDoS2019 *** evaluation demonstrated the potential of our model in robust intrusion detection in IoT networks,achieving an accuracy of 99.965%and a detection time of 0.20 s for the CIC-IoT2023 dataset,and 99.968%accuracy with a detection time of 0.23 s for the CIC-DDoS 2019 ***,a comparative analysis with recent related works highlighted the superiority of our methodology in in

关键词： DDoS Service networks

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